This preprint presents an adaptive hybrid MFO–TLBO optimization framework integrated with Extreme Learning Machine (ELM) for network intrusion detection. The proposed method combines the exploration capability of Moth-Flame Optimization (MFO) with the exploitation strength of Teaching-Learning-Based Optimization (TLBO) to enhance feature selection and classification performance. Experimental evaluation on the KDDCUP1999 dataset demonstrates that the proposed hybrid approach achieves 95.19% detection accuracy, outperforming several standalone optimization-based IDS models. This version represents a preliminary preprint. An extended version including evaluation on modern datasets such as CICIDS2018 will be submitted for journal publication.
Mohammed Kadhim Radhi Alaasam (Thu,) studied this question.